Study On Wind Power Prediction Method Based On Time Series Analysis

Wind power prediction is significant to electrical power system. An accurate forecasting can reduce the impact of wind power on power system, and increase the penetration of wind power and enhance the stability of power system operation. Because of the intermittence and fluctuation of the wind and the complexity of China’s topography, the wind condition is not stable relatively. So China’s wind power prediction is remained to be improved in accuracy.Under the circumstances, several models are studied and used to make the prediction based on Time Series Analysis in this paper. The main works are as follow:Statistical analysis of wind farm operation data is studied. Several single models and integrated models are established and the preprocessing is made before the establishment of models.An integrated model based on D-S Evidence Theory is put forward. The weights of single models are extracted as the evidence and the integrated model is established with multi-fusion of the belief function before the final result is presented. Then, the integrated model is optimized in the selection stage of single models.A recursive least square model based on auto-regulation forgetting factor is put forward. The result is predicted by recursive equation, and the auto-regulation forgetting factor is designed to adjust automatically according to the error.The wind power prediction model library is built based on optimized models and the optimization algorism of recursive prediction based on variable models is presented. The least relative error model is selected to predict wind speed or power, and the result is revised by recursive least square model based on auto-regulation forgetting factor, at least, the final result is more accurate.